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Introduction Rapid advances in data collection and storage technology have enabled or ganizations to accumulate vast amounts of data. However, extracting useful information has proven extremely challenging. Often, traditional data analy sis tools and techniques cannot be used because of the massive size of a data set. Sometimes, the non-traditional nature of the data means that traditional approaches cannot be applied even if the data set is relatively small. In other situations, the questions that need to be answered cannot be addressed using existing data analysis techniques, and thus, new methods need to be devel oped. Data mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It has also opened up exciting opportunities for exploring and analyzing new types of data and for analyzing old types of data in new ways. In this introductory chapter, we present an overview of data mining and outline the key topics to be covered in this book. We start with a description of some well-known applications that require new techniques for data analysis. Business Point-of-sale data collection (bar code scanners, radio frequency identification (RFID), and smart card technology) have allowed retailers to collect up-to-the-minute data about customer purchases at the checkout coun ters of their stores. Retailers can utilize this information, along with other business-critical data such as Web logs from e-commerce Web sites and cus tomer service records from call centers, to help them better understand the needs of their customers and make more informed business decisions. Data mining techniques can be used to support a wide range of business intelligence applications such as customer profiling, targeted marketing, work flow management, store layout, and fraud detection. It can also help retailers

Chapter 1. Introduction Pang-Ning Tan/Michael Steinbach/Vipin Kumar 1 Chapter 2. Data Pang-Ning Tan/Michael Steinbach/Vipin Kumar 19 Chapter 3. Exploring Data Pang-Ning Tan/Michael Steinbach/Vipin Kumar 97 Chapter 4. Classification: Basic Concepts, Decision Trees, and Model Evaluation Pang-Ning Tan/Michael Steinbach/Vipin Kumar 145 Chapter 5. Classification: Alternative Techniques Pang-Ning Tan/Michael Steinbach/Vipin Kumar 207 Chapter 6. Association Analysis: Basic Concepts and Algorithms Pang-Ning Tan/Michael Steinbach/Vipin Kumar 327 Chapter 7. Association Analysis: Advanced Concepts Pang-Ning Tan/Michael Steinbach/Vipin Kumar 415 Chapter 8. Cluster Analysis: Basic Concepts and Algorithms Pang-Ning Tan/Michael Steinbach/Vipin Kumar 487 Chapter 9. Cluster Analysis: Additional Issues and Algorithms Pang-Ning Tan/Michael Steinbach/Vipin Kumar 569 Chapter 10. Anomaly Detection Pang-Ning Tan/Michael Steinbach/Vipin Kumar 651 Appendix B: Dimensionality Reduction Pang-Ning Tan/Michael Steinbach/Vipin Kumar 685 Appendix D: Regression Pang-Ning Tan/Michael Steinbach/Vipin Kumar 703 Appendix E: Optimization Pang-Ning Tan/Michael Steinbach/Vipin Kumar 713 Copyright Permissions Pang-Ning Tan/Michael Steinbach/Vipin Kumar 724 Index 725

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